The Joint Modelling of Trip Timing and Mode Choice

Transcription

1 The Joint Modelling of Trip Timing and Mode Choice by Nicholas Day A thesis submitted in conformity with the requirements for the degree of Master of Applied Science Graduate Department of Civil Engineering University of Toronto Copyright by Nicholas Day 2008

2 The Joint Modelling of Trip Timing and Mode Choice Nicholas Day Master of Applied Science Graduate Department of Civil Engineering University of Toronto Abstract 2008 This thesis jointly models the 24 hour work trip timing and mode choice decisions of commuters in the Greater Toronto Area. A discrete continuous specification, with a multinomial logit model for mode choice and an accelerated time hazard model for trip timing, is used to allow for unrestricted correlation between these two fundamental decisions. Statistically significant correlations are found between mode choice and trip timing for work journeys with expected differences between modes. Furthermore, the joint models have a wide range of policy sensitive statistically significant parameters of intuitive sign and magnitude, revealing expected differences between workers of different occupation groups. Furthermore, the estimated models have a high degree of fit to observed cumulative departure and arrival time distribution functions and to observed mode choices. Finally, sensitivity tests have demonstrated that the model is capable of capturing peak spreading in response to increasing auto congestion. ii

3 Acknowledgements I would first like to thank Professor Eric Miller for his continued support and guidance not just during this thesis but throughout my career as an undergraduate and graduate student at the University of Toronto. Certainly, without his enthusiasm and passion for transportation it is unlikely that I would have even found the field. For this I am extremely grateful. I would also like to extend my gratitude to Dr. Khandker Nurul Habib for his invaluable assistance during the model specification and estimation stages of this thesis. Without his expertise in joint discrete continuous model theory and estimation, this thesis would not have been possible. Lastly, I would like to thank the Natural Sciences and Engineering Research Council (NSERC) of Canada and IBI Group for their financial support of this research through the Industrial Postgraduate Scholarship (IPS) program. In particular, I would like to thank Bruce Mori of IBI Group for initiating the IPS process and for his practical insights into the practical applicability of my research. iii

4 Table of Contents 1. INTRODUCTION 1 2. BACKGROUND AND MOTIVATION ACTIVITY SCHEDULING AND TRIP TIMING BEHAVIOUR PEAK SPREADING PEAK SPREADING IN THE GREATER TORONTO AREA TRIP TIMING IN TRANSPORTATION DEMAND MODELS JOINT WORK TRIP TIMING AND MODE CHOICE MODEL 7 3. DATA 9 4. EMPIRICAL ANALYSIS INTRODUCTION H W DEPARTURE TIME DISTRIBUTIONS WORK ARRIVAL TIME DISTRIBUTIONS CONCLUSION LITERATURE REVIEW INTRODUCTION EMPIRICAL MODELS DISCRETE CHOICE MODELS CONTINUOUS TIME MODELS JOINT DISCRETE CONTINUOUS MODELS SUMMARY AND CONCLUSION MODEL STRUCTURE INTRODUCTION MULTINOMIAL LOGIT (MNL) MODE CHOICE MODEL CONTINUOUS TIME HAZARD MODEL JOINT MODEL MODEL SPECIFICATION 44 iv

5 7.1 INTRODUCTION TOUR FEASIBILITY RULES MODE CHOICE FEASIBILITY RULES DATA PREPARATION AND EXPLANATORY VARIABLE DEFINITIONS MODEL ESTIMATION AND SPECIFICATION PROCESS MODEL ESTIMATION RESULTS INTRODUCTION JOINT HOME WORK DEPARTURE TIME AND MODE CHOICE MODEL JOINT HOME WORK ARRIVAL/START TIME AND MODE CHOICE MODEL SUMMARY AND CONCLUSIONS MODEL VALIDATION INTRODUCTION JOINT HOME WORK DEPARTURE TIME AND MODE CHOICE MODEL DISCRETE MODE CHOICE COMPONENT CONTINUOUS DEPARTURE TIME COMPONENT JOINT HOME WORK ARRIVAL/START TIME AND MODE CHOICE MODEL DISCRETE MODE CHOICE COMPONENT CONTINUOUS DEPARTURE TIME COMPONENT CONCLUSION MODEL APPLICATION MODEL SENSITIVITY ANALYSIS CONCLUSIONS AND FUTURE WORK REFERENCES APPENDIX DETAILED MODEL VALIDATION RESULTS JOINT H W DEPARTURE TIME MODEL JOINT H W ARRIVAL TIME MODEL 109 v

6 List of Tables TABLE 1. AM AND PM PEAK HOUR FACTORS BY REGIONAL BOUNDARY, 1975 TO TABLE 2. DISTRIBUTION OF TOURS BY OCCUPATION AND EMPLOYMENT TYPE... 9 TABLE 3. DISTRIBUTION OF WORK TOURS BY CHOSEN MODE AND OCCUPATION TABLE 4. MODE CHOICE AND TRIP TIMING VARIABLE DEFINITIONS TABLE 5. H W DEPARTURE TIME MODEL ESTIMATION SUMMARY BY OCCUPATION: KEY PARAMETERS AND GOODNESS OF FIT MEASURES TABLE 6. H W DEPARTURE TIME MODEL ESTIMATION SUMMARY BY OCCUPATION: LOGIT MODE CHOICE MODEL PARAMETERS TABLE 7. JOINT H W DEPARTURE TIME MODEL VALUE OF TIME (VOT) COMPARISON TABLE 8. H W DEPARTURE TIME MODEL ESTIMATION SUMMARY BY OCCUPATION: HAZARD MODEL COVARIATES TABLE 9. H W ARRIVAL TIME MODEL ESTIMATION SUMMARY BY OCCUPATION: KEY PARAMETERS AND GOODNESS OF FIT MEASURES. 60 TABLE 10. H W ARRIVAL TIME MODEL ESTIMATION SUMMARY BY OCCUPATION: LOGIT MODE CHOICE MODEL PARAMETERS TABLE 11. H W ARRIVAL TIME MODEL ESTIMATION SUMMARY BY OCCUPATION: HAZARD MODEL COVARIATES TABLE 12. JOINT H W DEPARTURE TIME MODE CHOICE CONFUSION MATRIX (PROFESSIONAL) TABLE 13. JOINT H W DEPARTURE TIME MODEL MODE CHOICE PERCENT CORRECT BY OCCUPATION GROUP TABLE 14. JOINT H W ARRIVAL TIME MODEL MODE CHOICE PERCENT CORRECT BY OCCUPATION GROUP vi

7 List of Figures FIGURE 1. PEAK SPREADING DUE TO CAPACITY CONSTRAINT... 3 FIGURE 2. OBSERVED CUMULATIVE HW TRIP DEPARTURES BY CHOSEN MODE FIGURE 3. OBSERVED CUMULATIVE HW TRIP DEPARTURES BY EMPLOYMENT TYPE FIGURE 4. OBSERVED CUMULATIVE HW TRIP DEPARTURES BY OCCUPATION TYPE FIGURE 5. OBSERVED CUMULATIVE HW TRIP DEPARTURES BY EMPLOYMENT AREA FIGURE 6. OBSERVED CUMULATIVE HW TRIP ARRIVALS BY CHOSEN MODE FIGURE 7. OBSERVED CUMULATIVE HW TRIP ARRIVALS BY EMPLOYMENT TYPE FIGURE 8. OBSERVED CUMULATIVE HW TRIP ARRIVALS BY OCCUPATION TYPE FIGURE 9. OBSERVED CUMULATIVE HW TRIP ARRIVALS BY EMPLOYMENT AREA FIGURE 10. H W CUMULATIVE PROFESSIONAL DEPARTURES (AUTO) FIGURE 11. H W CUMULATIVE PROFESSIONAL DEPARTURES (TRANSIT) FIGURE 12. H W CUMULATIVE PROFESSIONAL DEPARTURES (GO) FIGURE 13. H W CUMULATIVE PROFESSIONAL DEPARTURES (TRANSIT P&R) FIGURE 14. H W CUMULATIVE PROFESSIONAL DEPARTURES (WALK) FIGURE 15. H W CUMULATIVE PROFESSIONAL DEPARTURES (ALL MODES) FIGURE 16. H W CUMULATIVE MANUFACTURING DEPARTURES (ALL MODES) FIGURE 17. H W CUMULATIVE GENERAL OFFICE DEPARTURES (ALL MODES) FIGURE 18. H W CUMULATIVE RETAIL DEPARTURES (ALL MODES) FIGURE 19. H W CUMULATIVE PROFESSIONAL ARRIVALS (ALL MODES) FIGURE 20. H W CUMULATIVE MANUFACTURING ARRIVALS (ALL MODES) FIGURE 21. H W CUMULATIVE GENERAL OFFICE ARRIVALS (ALL MODES) FIGURE 22. H W CUMULATIVE RETAIL ARRIVALS (ALL MODES) FIGURE 23. JOINT H W DEPARTURE TIME MODEL ITERATIVE APPLICATION PROCEDURE FIGURE 24. MODEL SENSITIVITY: DAILY AUTO MODE SHARE FOR WORK TOURS BY CONGESTION SCENARIO FIGURE 25. MODEL SENSITIVITY: AUTO WORK TOUR PEAK HOUR FACTOR BY CONGESTION SCENARIO FIGURE 26. MODEL SENSITIVITY: PROPORTION OF DAILY AUTO TRIPS BY AM SHOULDER PEAK HOUR FIGURE 27. MODEL SENSITIVITY: CUMULATIVE H W AUTO DEPARTURES BY CONGESTION SCENARIO vii

8 1. Introduction The trip timing and mode choice decisions of commuters are critical considerations for transportation demand modellers. These two decisions directly determine the temporal distribution of demand experienced on any given piece of transportation infrastructure in an urban area. Furthermore, trip timing and mode choice decisions are inherently connected by the interactions between activity scheduling constraints and expected travel times to and from activity locations. Certainly, increasing congestion and transportation policies in modelled future year scenarios can be expected to influence both temporal and modal decision making. In spite of this fact, there have been very few studies in the transportation literature that have jointly modelled these fundamental travel decisions. In this thesis, two 24 hour joint models of Home Work (H W) journey timing and mode choice are estimated and validated using revealed preference survey data from the Greater Toronto Area (GTA), one for H W departure time choice, and another for H W arrival time choice. A joint discrete continuous model specification, with a continuous hazard model for H W trip timing and a multinomial logit model for mode choice, is used to capture the interconnected nature of mode choice and trip timing decisions. This thesis is organized into twelve chapters plus appendices. The following Chapter discusses the background behind and motivations for a joint model of work trip timing and mode choice. Next, Chapter 3 describes the data used to estimate the joint models, and outlines some of its important overall features. Chapter 4 analyzes the base year dataset in more detail, constructing observed departure and arrival distributions by occupation, employment, and mode choice. Chapter 5 presents a summary of previous literature related to the modelling effort in this thesis, specifically independent time of day choice models and joint models of trip timing and mode choice. Chapter 6 presents the theory behind the joint model and the individual mode choice and trip timing model structures used in this thesis. Chapter 7 discusses the tour and mode choice feasibility rules, data preparation, and model estimation methods used. Chapter 8 presents the detailed parameter estimation results of both joint models, and Chapter 9 presents the model validation results to observed base year data. Chapter 10 discusses method of applying the model. Finally, Chapter 11 evaluates the sensitivity of the departure time model to increasing congestion, travel times, and peak spreading, while Chapter 12 summarizes the overall results and conclusions. The last chapter also proposes avenues of future research. 1

9 2. Background and Motivation 2.1 Activity Scheduling and Trip Timing Behaviour The temporal distribution of the demand experienced on any given piece of transportation infrastructure is an aggregate result of the interactions between the activity scheduling patterns of individuals and transportation system capacity. There is a direct interdependence between trip scheduling and roadway congestion, with trip makers choosing their departure times based on expected travel times on their chosen mode of transport and their preferred start/arrival times 1 for each activity (Miller and Kriger, 2004). Furthermore, mode choice decisions are also made in the context of one s activity schedule; the feasibility and attractiveness of each mode is determined by the level of service (e.g. wait times, travel times, costs, etc.) offered to and from the planned activities at the desired time of travel. Therefore, when scheduling trips, individuals must jointly consider activity scheduling constraints alongside mode choice decisions and their associated impacts on travel times between activities. From a behavioural point of view, for each activity commuters must weigh the differing disutilities of arriving too early or too late, and the disutility of travel itself (Vickery, 1969). Since the travel times between activity locations are not known for certain, trip timing decisions are always made under uncertainty. Therefore, one selects a departure time based on previous experience, allowing for extra time between the expected arrival time and the required (or desired) activity start time depending on the expected congestion variability, the familiarity of the route, and the severity of the penalties for arriving late. For work trips in particular, commuters can be expected to select relatively narrow departure time windows due to clearly defined/required arrival times and their high familiarity with en route congestion and travel time variability. 2.2 Peak Spreading If the sum of the individual trip timing decisions choices of all commuters, the desired overall flow pattern, cause any given transportation facility to operate above or near the capacity, the actual flow pattern is spread out, compacting demand to the facility s capacity (Millerand Kriger, 2004), as shown in Figure 1. The phenomenon where trips are diverted to less congested time periods is known as peak spreading. 1 For example, preferred arrival times can be fixed or highly restricted by official workplace hours or be more flexible for recreational activities. 2

10 3 Figure 1. Peak Spreading due to Capacity Constraint (Miller et al., 2004) The Figure reveals the that peak spreading naturally occurs as a result of high travel demand continually exceeding a transportation corridor or facility s capacity, leading to significant traffic congestion, increased travel times, and an overall lengthening of the duration of the peak hour(s). In this case, individuals must adjust their departure times to earlier periods if they are to still arrive at their desired arrival time; commuters are simply responding to the added travel times caused by congestion. However, it also should be recognized that peak spreading can occur due to the conscious diversion of departures to shoulder peak or off peak hours to purposefully avoid travel during congested periods. Such behaviour requires flexibility in the timing of the given activity as it has the potential to significantly alter one s arrival time in relation to the preferred or required activity start time. For example, commuters with rigid work hours (e.g. shift jobs in manufacturing) must move their departure times proportionately earlier in response to travel time increases. Commuters with more flexible workplace hours, on the other hand, would be less constrained and would have the option of departing earlier, later, or even at the same time depending on the level of flexibility available and the level of congestion associated with each choice. For work trips, mild degrees of work arrival/start time flexibility can be expected, depending on the nature of the job. However, the ability to completely avoid travelling during peak periods (especially to later periods) is unlikely except for select specialized professional workers that have the highest degrees of work hour flexibility (or if the person does not have work hours during the peak period in the first place). Lastly, it should be noted that this discussion of peak spreading has only considered a single mode; when alternate mode choices exists individuals also have the option of switching modes when travel times increase on their typical mode of travel.

11 4 An important measure of the extent of peak spreading experienced on a given transportation corridor/facility is the Peak Hour Factor (PHF), which is the ratio of trips made during the peak hour to the trips made during the overall three hour peak period. PHF values of one third, which imply an equal distribution of trip making during all three hours of the peak period, represent the highest degrees of peak spreading and progressively higher values represent more peaked situations. It should be noted that the precise definitions of the three hour peak period and peak hour depend on the nature of the urban area and transportation facility in question and on whether one is considering the am or pm peak. 2.3 Peak Spreading in the Greater Toronto Area In order to assess the severity of peak spreading in the Greater Toronto Area (GTA), cordon count data was used to calculate the Peak Hour Factor (PHF), as defined in the previous Section, for the years 1975 to 2004 at key regional boundaries for both the am and pm peak periods. For the purposes of this analysis, the peak three hour and one hour periods for each screenline for each year were defined as the contiguous three hour and one hour periods yielding the highest traffic volume between 6:00 10:00 am and 16:00 20:00 pm. Table 1. AM and PM Peak Hour Factors by Regional Boundary, 1975 to 2004 AM Peak Hour Factor PM Peak Hour Factor Screenline % Change % Change Peel Toronto % % York Toronto % % Durham Toronto % % Toronto Downtown % % Average % % From Table 1, peak spreading is evident across all regional boundaries, with the PHF decreasing approximately 10% on average as one moves from 1957 to It is interesting to note that the Toronto Downtown screenline had the smallest decrease in the value of the am PHF during the survey years, remaining constant at approximately This reflects the continued high levels of congestion encountered when crossing this screenline during the am peak period throughout all of the included time periods; the full extent of peak spreading has already occurred.

12 5 As congestion and travel times increase in modelled future year scenarios, it is expected that peak spreading will intensify, further shifting travel away from the traditional peak hours. Increasing congestion will also influence the mode choices of individuals, dissuading auto trips as travel times increase. Furthermore, Transportation Demand Measures (TDM) and other transportation policies can be expected to influence both the attractiveness of individual modes and the temporal distribution of travel. Congestion pricing policies, for example, attempt to artificially induce peak spreading and modal shifts by introducing a monetary cost to travel by the auto mode during peak periods. Certainly, any credible transportation demand model must be sensitive to the effects of congestion on both the trip timing and mode choice decisions of commuters. 2.4 Trip Timing in Transportation Demand Models Despite the importance of the impacts of trip timing decisions on the distribution of transportation demand, in practice most transportation demand models used at the regional planning level do not explicitly model the temporal dimension of travel. Indeed, conventional four stage transportation demand models, which typically model demand for the morning peak hour in isolation by applying simplistic peaking factors to convert total predicted daily demand into peak hour demand, remain as the standard of practise approach. For example, the latest version of the Southern California Association of Governments (SCAG) four stage Regional Transportation Model (SCAG, 2003) for the Greater Los Angeles Area uses peak and off peak factors derived from origin destination survey data to subdivide generated trips by purpose into the model s four time periods (am peak, pm peak, mid day, and evening) for trip assignment. Similarly, the Greater Toronto Area s GTAModel (Miller, 2007), uses observed average trip rates calculated on an origin to destination basis at the planning district level to generate daily 24 hour and morning peak period journeys. In both cases, the fixed peaking factors calculated from observed base year data are inherently insensitive to the changing conditions and policies of future year scenarios. This severely limits the ability of such models to accurately predict the temporal distribution of trips in future year scenarios. From the GTA screenline data previously presented in Table 1, peak hour demand would be over predicted by at least 10% in 2004 if a constant peak hour factor was used in a conventional transportation demand model estimated from 1975 data. Although most practical implementations of transportation demand models continue to use the traditional four stage trip based approach, the majority of recent transportation research has been focussed on more advanced activity based models, such as ALBATROSS (A Learning Based Transportation Oriented Simulation System), TASHA (Travel Activity Scheduler for Household Agents),

13 6 CEMDAP (Comprehensive Econometric Micro simulator for Daily Activity travel Patterns), and FAMOS (Florida Activity Mobility Option Simulator) 2. By recognizing that people travel in order to participate in activities, activity based models have the ability to holistically model the skeletal components of an individual s entire tour of travel for the day, explicitly recognizing scheduling constraints and household resource allocation conflicts. Activity based models are also starting to become more commonplace in practise due to their more behaviourally sound treatment of travel demand and the continually increased demands that are being placed on transportation demand models 3 (Davidson et al., 2007). Although activity based models are capable of considering the detailed impacts of scheduling constraints on trip timing and mode choice at both the household and individual level, significant problems remain in the generation of policy sensitive distributions of activity frequencies and start times. These activity start times, particularly the skeletal Home Work Home activity components, are key inputs to the activity scheduler. In TASHA, for example, activity frequencies, start times, and durations are randomly drawn from observed base year distributions, which are cross classified by activity type, person, household, and schedule attributes, to generate the randomness of each skeletal component across the population (Roorda et al., 2008). Although this approach is sufficient in the base year and for short term forecasting, it is insensitive to significant changes in the distribution of activity start times (and activity participation rates). The resulting peak spreading from increasing congestion in future years, for example, would not be fully accounted for. The use of base year distributions is also inappropriate when evaluating short term policies that would have the potential to significantly alter activity schedules, such as peak period congestion pricing and the widespread introduction of flexible work hours. Activity based models by (Vovsha and Bradley, 2004) and (Bowman and Ben Akiva, 2000), on the other hand, are examples of tour based models that incorporate an explicit time of day choice component in their nested logit choice structures to simultaneously predict one s departure time from home and arrival time back home. In both cases time is represented as a discrete choice between a combined set of home departure and home arrival time periods: Bowman and Ben Akiva consider four broad time periods (am peak, midday, pm peak, and other), while Vovsha and Bradley use an increased temporal resolution by considering all nineteen individual hours between 5:00am and 11:00pm. Both models are applied sequentially through the use of priority rules defined by tour activity type (e.g. 2 See (Arentze and Timmermans, 2004), (Miller and Roorda, 2003), (Bhat et al., 2004), and (Pendyala et al., 2005) respectively for more details on each respective model. 3 For example, detailed emissions modelling, congestion pricing, and vehicle occupancy analysis.

14 7 primary/mandatory tours, followed by secondary/non mandatory tour purposes) to ensure that the time periods available in each subsequently scheduled tour can be restricted to alternatives that do not overlap with already scheduled tours. The utility based discrete choice framework used in both models introduces policy sensitivity to the temporal choice dimension of individuals through the use of person, household, and travel related attributes. Although this discrete choice approach represents a significant improvement over the use of base year distributions, as will be discussed in Section 5.4, significant limitations remain with the representation of time as a discrete variable. Overall the treatment of time in activity based models is significantly superior to that of traditional four stage models, which typically do not consider trip timing at all. However, issues associated with the use of observed base year distributions to generate skeletal activity components and, to a lesser extent, the use of discrete choice constructs, bring the forecasting abilities of activity based models into question. A better understanding and representation of the distribution of activity start times is required. 2.5 Joint Work Trip Timing and Mode Choice Model The joint work trip timing and mode choice model estimated in this thesis is to serve as a bridge between state of the art activity based and traditional four stage transportation demand models. It can be used to effectively improve upon and serve as an input to both modelling paradigms. It represents a significant improvement over standard practice four stage transportation demand models in that it explicitly models the temporal dimension of travel at the individual level. The mode choice component of the model also moves a step closer to a full tour based approach by explicitly considering the Home Work and Work Home components of one s Home Work Home journey. The model is also expected to improve current state of the art activity based models by increasing the sensitivity of their skeletal activity generation components to policies and changes to base year conditions (Habib and Miller 2006). Since the joint model has the capability of fully microsimulating the joint trip timing and mode choices of individuals, it is envisioned that the model can be used to directly generate the inputs to the activity scheduler of an activity based model. Most importantly, the joint model formulation of this thesis recognizes the fact that work trip timing and mode choice are inherently correlated and must be modelled jointly to adequately assess the full impacts of increasing congestion and transportation policies that affect both the modal and temporal dimensions of travel. Most state of the art models in the literature consider activity scheduling and trip timing in isolation from mode choice decisions. Certainly, the phenomenon of peak spreading cannot be

15 8 adequately modelled from a behavioural point of view without a joint model that considers the impact of congestion on mode choices and on trip timing. From a practical standpoint it should be recognized that it is equivalent to model H W departure time decisions or H W arrival times; both times are connected by the travel time between the trip s origin and destination. A priori however, it is unclear which approach is more behaviourally sound. On the one hand, it is more natural to describe departure time decisions since this is the choice that each commuter makes every day. Indeed, this is the approach selected by the vast majority of work trip timing literature, especially for models that work within the traditional trip based framework. However, it can be equally argued that departure times are more or less fixed by one s chosen (or prescribed) work start time and the expected travel time to work. This is the approach that is most likely to be inline with the requirements of an activity based transportation demand model and with the thought processes of individual commuters. Both approaches are evaluated in the models estimated in this thesis; two models are estimated, one for H W departure time and another for H W arrival time.

16 3. Data A subset of the 2001 Transportation Tomorrow Survey (TTS), a multimodal travel survey conducted in the Greater Toronto Area (GTA) every five years, was used as the base data for the model estimated herein. The TTS survey records the detailed travel records of a random 5% sample of households within the GTA for a single day (DMG, 2005). The survey provided all individual level trip data for the model, including trip start times, home and work locations, mode choices, and household/person level socioeconomic attributes. It should be noted that trip end times (or alternatively activity arrival/start times) are not collected as part of the TTS; only trip start times are recorded. Therefore, work trip arrival times and durations were constructed through the use of predicted zone to zone network auto and transit travel times obtained from an EMME/2 assignment model and reported trip start times and mode choices. Auto travel times were computed for the complete 24 hour day, with one EMME/2 trip assignment conducted for each hour. For transit modes, morning peak hour level of service variables were used during all time periods due to the lack of a readily available off peak GTA transit network coded into EMME/2. The use of peak period level of service variables during off peak periods can be expected to underestimate wait times due to lower service frequencies and overestimate in vehicle travel times on surface routes due to lower traffic congestion. On the whole, it should overestimate the attractiveness of the transit modes since the added wait times are expected to be much more significant than the shortened in vehicle travel times during off peak hours. After controlling for missing values and applying tour and mode feasibility rules (see Sections 7.2 and 7.3 for more details) 102,975 individual Home Work Home tour records were available for model estimation. The distribution of individual observations by occupation and employment type is shown in Table 2 below. Table 2. Distribution of Tours by Occupation and Employment Type Occupation Type \ Employment Type Full Time Part Time Total Obs. % Obs. % Obs. % General Office / Clerical 11,463 12% 1,829 18% 13,292 13% Manufacturing / Construction / Trades 20,995 23% 1,366 13% 22,361 22% Professional / Management / Technical 46,444 50% 2,941 28% 49,385 48% Retail Sales and Service 13,706 15% 4,231 41% 17,937 17% Total 92, % 10, % 102, % 9

17 10 The Table reveals that the dataset is dominated by full time workers, accounting for over 90% of all individuals, while the largest occupation group is professionals, employing almost 50% of all workers. It is also interesting to note that most part time workers are employed in the retail sector. Table 3, below, outlines the distribution of observed mode choices by occupation type. Table 3. Distribution of Work Tours by Chosen Mode and Occupation General Office Manufacturing Professional Retail Total Mode Obs. % Obs. % Obs. % Obs. % Obs. % Auto Drive 8, % 17, % 37, % 11, % 75, % Auto Passenger 1, % 2, % 2, % 1, % 7, % Local Transit 2, % 2, % 6, % 3, % 14, % Transit P&R % % % % % GO Rail P&R % % 1, % % 1, % Walk / Bike % % 1, % % 3, % Total 13,292 22,361 49,385 17, ,975 Overall, the auto drive mode accounts for the vast majority of work tours, which is to be expected since the dataset considers work trips for the full 24 hours of the day, not just the peak periods. The auto mode is dominant most periods of the day but especially during off peak hours when transit service is at its lowest levels. It also should be noted that identification issues may occur during model estimation for the Park and Ride (P&R) modes due to their comparatively small number of observations, especially if models are estimated by specific occupation and/or employment types; approximately 50 transit and GO P&R trips were observed among individuals employed in the Manufacturing sector. Indeed, the GO transit access mode, which is not shown here, was removed from consideration altogether due to its very small number of observations; approximately 700 total observations before controlling for tour and mode feasibility rules.

18 4. Empirical Analysis 4.1 Introduction This Chapter analyzes the base Home Work Home TTS tour data to gain an understanding of the distribution of existing work trip timing trends and the factors that influence trip scheduling decisions. Cumulative distribution functions (CDF) of observed Home Work departure times and constructed work arrival/start times are analyzed by employment and occupation type, mode choice, and employment location. As discussed in Chapter 0, work arrival/start times are constructed by adding modelled travel times (on the observed mode) to observed departure times. Sections 4.2 and 4.3 below present and discuss the features o observed TTS H W departures and H W arrivals respectively. This empirical analysis is a significant first step before moving on to detailed model specification. Indeed, any estimated model of H W trip timing will attempt to reproduce these observed cumulative distribution functions. In addition, microsimulation methods used during model application directly make use of modelled CDFs to make individual predictions. Therefore, it is critical that all significant trends in the observed CDFs are recognized during model estimation to ensure a high level of fit to observed data and to ensure increased sensitivity to changing conditions in forecasting situations. In particular, the empirical analysis helps the modeller identify potential explanatory variables to introduce into the model and cases where heterogeneity between distinct user groups in the population merit separate parameter estimations. 4.2 H W Departure Time Distributions Figure 2, below, outlines the cumulative distribution of observed home departure times for work trips by chosen mode. From the Figure, it is evident that significantly different departure time profiles are observed between modes. Individuals selecting the GO Rail and Local Transit Park and Ride modes have the earliest departure times, with most users departing from home before 9:00 am. This is to be expected given the competition for limited parking spaces at both GO and local transit commuter parking lots, and the fact that no train service exists past 9:00 am on most GO commuter lines. Individuals using the Walk/Bike mode, on the other hand, have the latest home departure times, as expected. Non motorized trips are typically short, with short travel times, since longer distance travel is typically infeasible. Finally, the local transit and auto modes (drive and passenger) exhibit similar distributions in between that of the previously discussed modes, with the vast majority of work trips departing before 10:00 am. It also should be noted that all CDFs, except for the highly constrained Park 11

19 12 and Ride modes, have three major departure time periods: morning, afternoon and evening. The vast majority of individuals depart during morning time periods that coincide with traditional work start times in offices. The afternoon and evening departure time periods, on the other hand, coincide with the work start times for professions with afternoon/evening shift work (e.g. manufacturing and retail). Overall, the observed trends are reflective of the spatial and temporal markets that each mode serves. Indeed, it is evident from the observed CDFs that mode choice and work trip departure times are interrelated. The joint model specification appears to be merited. Figure 2. Observed Cumulative HW Trip Departures by Chosen Mode % 90.00% 80.00% Proportion of Trips 70.00% 60.00% 50.00% 40.00% 30.00% 20.00% 10.00% 0.00% 0:00 2:00 4:00 6:00 8:00 10:00 12:00 14:00 16:00 18:00 20:00 22:00 0:00 Departure Time Drive Passenger GO Rail P&R Local Transit Local Transit P&R Walk/Bike Figure 3, below, outlines the CDF of observed home work departure times for work trips by employment type. As expected, significant differences in the distribution of home departure times are observed between full time and part time workers. Full time workers predominantly depart during traditional morning work periods, while part time workers have significant departures in both the morning and afternoon/evening periods. Furthermore, part time workers generally appear to depart later, even for earlier morning work trips. Both of these trends are to be expected since the majority of part time workers are employed in the retail sector (as previously shown in Table 2), which generally has work shifts spread throughout the day ranging from 9:00am to 9:00pm. Despite the fact that there are

20 13 significant differences in the departure time distributions of full and part time workers, it should be noted that the overall combined distribution is practically identical to the full time worker curve since only 10% of all workers in the dataset are employed part time. Even so, the differences in the homework departure patterns of full and part time workers need to be recognized. Figure 3. Observed Cumulative HW Trip Departures by Employment Type Proportion of Trips % 90.00% 80.00% 70.00% 60.00% 50.00% 40.00% 30.00% 20.00% 10.00% 0.00% 0:00 2:00 4:00 6:00 8:00 10:00 12:00 14:00 16:00 18:00 20:00 22:00 0:00 Departure Time Full Time Part Time Figure 4, below, outlines the CDF of observed home work departure times by occupation type. The Figure reveals that individuals with different occupation types have departure distributions of considerably different overall shape and timing. Individuals employed in the manufacturing/trades sector have the earliest departure times, and three clearly distinct periods of departure, which is a result of the presence of the fixed shifts in manufacturing workplaces (morning, afternoon, evening). Professionals and general office workers, on the other hand, have nearly indistinguishable departure time trends. This is to be expected since both groups are likely to be employed in similar workplaces. Finally, the retail sector has the least peaked CDF; its trip departures are spread the most uniformly throughout the day. This is a result of the significance of part time employment in the retail sector and workplace hours that typically span the entire day, including distinct morning, afternoon, and evening shifts. Overall, Figure 4 demonstrates high degrees of heterogeneity between workers of different employment types. Separate model estimations by occupation group appear to be merited.

21 14 Figure 4. Observed Cumulative HW Trip Departures by Occupation Type Proportion of Trips % 90.00% 80.00% 70.00% 60.00% 50.00% 40.00% 30.00% 20.00% 10.00% 0.00% 0:00 2:00 4:00 6:00 8:00 10:00 12:00 14:00 16:00 18:00 20:00 22:00 0:00 Departure Time General Office Manufacturing/Trades Professional Retail Figure 5, below, outlines the CDF of observed home work departure times by employment area.

22 15 Figure 5. Observed Cumulative HW Trip Departures by Employment Area 100% 90% 80% 70% Proportion 60% 50% 40% 30% 20% 10% 0% 0:00 2:00 4:00 6:00 8:00 10:00 12:00 14:00 16:00 18:00 20:00 22:00 0:00 Departure Time Downtown Toronto GTA Hamilton From the Figure, it is apparent that employment location has very little impact on the distribution of home work departures. The most significant difference is for home work trips destined to downtown Toronto, which have slightly less early morning departures and slightly more late morning departures. This is likely due the fact that individuals working in the downtown core are more likely to use transit modes (especially GO Park and Ride) and be employed in a professional or general office workplace. Indeed, manufacturing workplaces, which have been previously shown to have the earliest departures (Figure 4), are typically located in more suburban areas, while the Park and Ride modes have been shown to have a large number of late morning departures (Figure 2). Overall, employment location does not appear to be a dominant factor in determining work trip timing; differences by mode choice, occupation, and employment type are more important considerations. 4.3 Work Arrival Time Distributions Figure 6, below, outlines the cumulative distribution of constructed work arrivals by chosen mode.

23 16 Figure 6. Observed Cumulative HW Trip Arrivals by Chosen Mode % 90.00% 80.00% 70.00% Proportion 60.00% 50.00% 40.00% 30.00% 20.00% 10.00% 0.00% 0:00 2:00 4:00 6:00 8:00 10:00 12:00 14:00 16:00 18:00 20:00 22:00 0:00 Work Arrival Time Drive Passenger GO Rail P&R Local Transit Local Transit P&R Walk/Bike As was the case with H W departures, commuters with different mode choices have significantly different work arrival distributions. From the Figure, auto users arrive at work the earliest. The effects of early morning (manufacturing) and afternoon/evening (manufacturing and retail) work start times are present in the auto CDFs and to a lesser extent in the local transit and walk curves. Once again, the GO and Local transit park and ride modes are constrained to limited arrival windows that are consistent with the traditional work start hours of general office and professional workplaces (between 8:00am and noon) and GO operating hours. Indeed, P&R and transit users in general are found to arrive later than auto users. This is due to a combination of factors. Firstly, transit modes are less attractive during off peak travel periods due to lower service levels. Secondly, occupations with earlier work start times, such as manufacturing/trades, are more likely to be located in areas without readily accessible transit. Furthermore, professional workers in downtown areas are more likely to use transit, due to the presence of higher order transit services, and generally have more flexible arrival times. It also should be noted that the computed work arrival times do not necessarily directly correspond to official workplace hours. Commuters can be expected to leave extra time between their anticipated and officially required arrival times. For example, auto users may be expected to leave more time between their required and actual times to account for unexpected congestion. This could further explain the differences in the

24 17 distributions between modes. Regardless of the behavioural mechanisms behind the different work arrival distributions, it is clear that significant differences in work arrival times are observed depending on one s choice of mode. It also should be recognized that the work arrival time distribution functions in this section are considerably smoother than the home departure time distributions presented in the previous section. This is at least in part due to the fact that the home departure times are directly reported by individuals during the survey and are likely to be rounded to the nearest 15 minute interval by survey respondents, causing the step like CDFs for the observed departure times. When constructing arrival times, however, fully continuous modelled travel times are added to the reported departure time. This alone has the tendency to decrease the stepped nature of the CDF for arrivals. However, the complete lack of a steplike shape in the CDF for arrivals implies that arrivals are more fixed in nature with comparatively less variability between individuals. This can be expected because work arrival times are closely related to official workplace start hours, which are more or less fixed depending on one s workplace. Home departure times vary considerably more because individuals are dispersed throughout the urban area with very different travel times by mode. Figure 7, below, outlines the cumulative distribution of computed work arrivals by employment type. Figure 7. Observed Cumulative HW Trip Arrivals by Employment Type Proportion % 90.00% 80.00% 70.00% 60.00% 50.00% 40.00% 30.00% 20.00% 10.00% 0.00% 0:00 2:00 4:00 6:00 8:00 10:00 12:00 14:00 16:00 18:00 20:00 22:00 0:00 Work Arrival Time Full Time Part Time

25 18 The Figure reveals that full time workers predominantly arrive in the morning hours before 10:00am, while part time workers generally depart later and are more equally split between morning, afternoon, and evening employment hours. This is consistent with expectations and with the conclusions drawn from the equivalent departure time distribution in the previous section. Figure 8, below, outlines the cumulative distribution of computed work arrivals by occupation type. Figure 8. Observed Cumulative HW Trip Arrivals by Occupation Type % 90.00% 80.00% 70.00% Proportion 60.00% 50.00% 40.00% 30.00% 20.00% 10.00% 0.00% 0:00 2:00 4:00 6:00 8:00 10:00 12:00 14:00 16:00 18:00 20:00 22:00 0:00 Work Arrival Time General Office Manufacturing/Trades Professional Retail Once again, significant differences are found in the arrival time distributions for each occupation. The trends are practically identical to those revealed in the equivalent home departure time distribution presented in the previous section. Indeed, the professional and general office distributions are nearly identical with most arrivals occurring during traditional morning office work start times between 8:00am and 10:00am. As expected, the retail curve is most like that of the part time CDF with work arrivals spread throughout the day. Finally, the manufacturing CDF has a significant number of arrivals in the early morning hours before 8:00 am and an appreciable number of afternoon and evening arrivals beyond 2:00 pm, which is a result of the shift work that is typical at manufacturing workplaces. Figure 9, below, outlines the cumulative distribution of computed work arrivals by employment area.

26 19 Figure 9. Observed Cumulative HW Trip Arrivals by Employment Area 100% 90% 80% 70% Proportion 60% 50% 40% 30% 20% 10% 0% 0:00 2:00 4:00 6:00 8:00 10:00 12:00 14:00 16:00 18:00 20:00 22:00 0:00 Arrival Time Downtown Toronto GTA Hamilton In this case, the Figure reveals a slight difference in the arrival trends of workers destined for downtown Toronto. The other regions, however, show nearly identical shapes, which was also the case in the home departure time distributions by employment area presented in the previous section. The differences observed here for downtown employment areas are a result of the fact that most downtown workplaces employ professional and general office workers with traditional morning office work start hours (between 8:00 am and 10:00 am). As was shown in Figure 8, work start times outside of these periods are more likely in manufacturing/trades occupations, which are primarily located in suburban areas outside of the downtown core. 4.4 Conclusion This Chapter has revealed that one s chosen mode of travel significantly affects the implied distribution of home work departure times and work arrival times. The observed home work departure and arrival time distributions have demonstrated that mode choice and trip timing are correlated and hence a joint model, as proposed by this thesis, is merited. This is not surprising since each mode s feasibility and attractiveness is dependent on one s planned time period of travel. Park and Ride modes, for example, are highly restricted to early morning departures due to the competition for limited parking spaces at commuter lots and the absence of GO commuter rail service on most lines during off peak hours.

27 20 Indeed, all types of transit are particularly unattractive during off peak periods due to lower levels of service. Furthermore, all modes also compete with each other in different spatial markets. For example, transit is generally more attractive for downtown destinations, while the auto mode is much more attractive for accessing more suburban workplaces. Overall, the observed cumulative distribution functions presented in this Chapter reflect both the geographic and temporal markets that each mode serves. Furthermore, significant degrees of heterogeneity were observed between individuals with different employment and occupation types. As one would expect, professional and general office workers have similar trip timing patterns that are primarily focussed within the confines of the traditional 9:00am to 5:00pm work day. Manufacturing/trades and retail workers, on the other hand, have significantly different cumulative distributions with work departures and arrivals spread throughout the day into very sharply defined work shifts. Certainly, these significant differences between occupation groups must be recognized by the joint model estimated in this thesis. Indeed, separate joint model estimations by occupation type are conducted in this thesis; covariates affects are unlikely to account for these differences on their own. Although significant differences were also observed between full and part time workers, the overall distribution across all workers, independent of employment type, remains very similar to the full time worker CDF since only 10% of the workers in the data set are employed part time. As such it is not as essential to capture the observed heterogeneity between individuals with different employment types. This is especially true if one sufficiently accounts for the heterogeneity in the retail sector since the majority of part time workers are employed in retail workplaces. In this thesis, separate model estimations were attempted by employment type but difficulties were encountered with including the Park and Ride modes in the model due to the limited number of observations for those modes in comparison to the others (especially among part time workers). As such, in this thesis the differences between full and part time workers are ultimately captured through covariate effects (dummy variable). Lastly, significant differences in trip timing trends were not observed by employment location. The differences that were observed, for work trips destined to downtown Toronto in particular, can be explained by other factors. For example, the trip timing distributions are affected by the fact that transit use is considerably more attractive for trips destined to the downtown due to the availability of higher order transit services and that downtown employment is dominated by the professional and general office sectors.

28 5. Literature Review 5.1 Introduction There is a considerable history of transportation mode choice studies with a significant depth of literature and many implementations used in practise at the regional planning level. The study of trip timing decisions, on the other hand, is comparatively young with few studies in the literature and even fewer practical implementations. Indeed, most current standard of practice transportation demand models do not explicitly recognize the temporal dimension of travel. Over the past decade, however, there has been increasing interest in modelling the temporal distribution of trips and activities, especially among transportation researchers studying activity based transportation demand models. This Chapter presents an overview of a selection of papers directly related to this thesis, discussing key findings, issues, and lessons learned. In particular, the following sections focus on papers that model trip timing decisions on their own and joint models that consider trip timing and mode choice together. Due to the well established nature of mode choice research in the transportation literature, papers that independently model mode choice on its own are not discussed herein. Please refer to (Ben Akiva and Lerman, 1985) for a more detailed discussion of discrete choice models and specifically their application to commuter mode choice. 5.2 Empirical Models As previously discussed in Section 2.4, most conventional four stage transportation demand models do not explicitly account for trip scheduling considerations. They typically predict peak hour travel demand as a constant percentage of the predicted daily demand through the use of peaking factors estimated from base year data. Certainly, the accuracy of such fixed factors is questionable, especially in the forecasting context. The next logical improvement to traditional models is to directly relate the peaking factors to congestion measures, thereby modelling the peak spreading phenomenon at the aggregate level. One of the simplest and most common improvement approaches estimates peaking factors for specific highway segments at the link level using linear regression on observed traffic count data. Separate estimations of parameters can be undertaken by roadway type, number of lanes, regional location, and area type. Studies applying this approach, including (Loudon et al., 1988) and (Ivan et al., 2001), have demonstrated a clear and consistent pattern of peak spreading on highway facilities as congestion increases. Despite promising results, link based models can have problems with continuity of flow when 21